Coordinate Based

Coordinate-based methods represent data points using their spatial coordinates as input to neural networks, aiming to learn continuous functions that map coordinates to desired outputs (e.g., color, depth, or other features). Current research focuses on improving the efficiency and accuracy of these methods, particularly through novel architectures like implicit neural representations (INRs) and the development of specialized loss functions and activation functions tailored to specific applications. This approach shows promise for various fields, including 3D modeling, image processing, and scientific data analysis, by offering efficient representations and enabling tasks like high-fidelity image generation, accurate 3D reconstruction from sparse data, and improved seismic inversion.